Disease-specific Preference-based Measure of Glaucoma Health States: HUG-5 Psychometric Validation
Bibliographic record
Abstract
PURPOSE: Glaucoma is the second leading cause of irreversible blindness in the world, with 60 million people worldwide estimated to suffer from the condition. Health utility is an important outcome measure of treatment effect in economic outcomes. The available tools for the measurement of health utility in glaucoma patients do not support a preference-based algorithm required to estimate health utility. To resolve this gap in the literature, the HUG-5 (Health Utility for Glaucoma-5 dimensions) was developed. The objective of the present study was to validate the HUG-5 with accepted measures of health state and vision-specific quality of life. MATERIALS AND METHODS: The HUG-5 measures patient self-reported levels of visual discomfort, mobility, daily life activities, emotion, and social activities, as affected by the progression and management of glaucoma. To evaluate the psychometric properties, the HUG-5 was assessed for construct validity between similar and dissimilar dimensions of the National Eye Institute Visual Function Questionnaire-25 (NEI-VFQ-25) and the EuroQol's 5 Dimensions. The HUG-5 was evaluated for test-retest reliability after a 2-week period. The HUG-5 composite distributions of mild/moderate and advanced glaucoma patients were tested for differences to measure sensitivity. RESULTS: A total of 124 patients with glaucoma were administered the NEI-VFQ-25, the HUG-5, and the EuroQol's 5 Dimensions. The HUG-5 demonstrated construct validity, with convergent and discriminant support for visual discomfort, mobility, daily life activities, emotional distress, and social activities. The HUG-5 concurrently measured health-related quality of life associated with best-eye visual field loss (r=0.63, P<0.001). The HUG-5 measured health state consistently with test-retest reliability (intraclass correlation=0.91, P<0.001). The HUG-5 was established to be sensitive in detecting differences between patients with mild/moderate glaucoma and those with advanced glaucoma with a rank-sum test with continuity correction (W=693.5, P<0.001). CONCLUSIONS: This study demonstrates promising results for the HUG-5's response range and relationship with the NEI-VFQ-25 and best-eye visual field loss, highlighting the value of disease-specific preference-based scoring systems in measuring health state changes in glaucoma patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".